A wireless communication receiver estimates the actual signal distorting characteristics of the propagation channel over which it receives a signal. These signal distorting characteristics are represented by the channel transfer function, also referred to as simply the channel response. By estimating the channel response, the receiver can compensate the received signal for channel-induced distortion in order to improve data extraction.
Filtering an initial estimate of the channel response to suppress noise generally produces an estimate with better quality. This suggests that filtering should be performed to a greater extent (e.g., over a longer period of time) as the noisiness of the initial estimate increases. On the other hand, filtering the initiate estimate hampers tracking of the channel response as the response changes. This suggests that filtering should be performed to a lesser extent as the variation rate of the channel response increases, e.g., as the receiver velocity increases. Known estimation approaches therefore adapt the extent of filtering to address both initial estimate noise and channel response tracking.
Despite adapting filtering in this way, many conventional approaches still prove to be suboptimal when the channel response is formed from multiple channel coefficients. In a Direct Sequence Code Division Multiple Access (DS-CDMA) system, for instance, the channel response is typically formed from multiple channel coefficients corresponding to different path delays. And in an Orthogonal Frequency Division Multiplexing (OFDM) system, the channel response is formed from multiple channel coefficients corresponding to different locations in the time-frequency grid. Regardless of the system type, the conventional approaches filter initial estimates for all of the channel coefficients to the same extent. This uniform filtering across all channel coefficients produces suboptimal filtered estimates because the initial estimates of different channel coefficients often have different degrees of noisiness, meaning that at least some of the initial estimates are filtered to a greater or lesser extent than actually needed.
Other approaches provide better filtered estimates by filtering initial estimates of different channel coefficients to different extents. See, e.g., U.S. Pat. No. 7,428,262, U.S. Pat. Pub. No. 2006/0128326, and U.S. Pat. No. 7,848,463. However, certain aspects of these approaches are still suboptimal. Indeed, some of the approaches rely on an offline evaluation of the filtering extent that is optimal for different channel coefficients under different conditions, meaning that the receiver is burdened with maintaining multi-dimensional look-up tables. Other approaches inefficiently test filtering the initial estimates with different candidate filter configurations and use whichever filtered estimates have the best quality.